Performansi Algoritma CODEQ dalam Penyelesaian Vehicle Routing Problem
Abstract: Genetic Algorithm,
Tabu Search, Simulated Annealing, and Ant Colony Optimization showed a good
performance in solving vehicle routing problem. However, the generated solution
of those
algorithms was changeable
regarding on the
input parameter of
each algorithm. CODEQ is a new,
parameter free meta-heuristic algorithm that had been successfully used to solve constrained
optimization problems, integer
programming, and feed-forward
neural network. The purpose of this research are improving CODEQ
algorithm to solve vehicle routing problem and testing the performance of the
improved algorithm. CODEQ algorithm is started with population initiation as
initial solution, generated of mutant vector for each parent in every iteration,
replacement of parent by mutant when fitness function value of mutant is better
than parent’s, generated of new vector for each iteration based on opposition
value or chaos principle, replacement of worst solution by new vector when
fitness function value of new vector is better, iteration ceasing
when stooping criterion
is achieved, and
sub-tour determination based
on vehicle capacity constraint. The result showed that the average
deviation of the best-known and the
best-test value is
6.35%. Therefore, CODEQ
algorithm is good
in solving vehicle
routing problem.
Penulis: Annisa Kesy Garside,
Satya Sudaningtyas
Kode Jurnal: jptindustridd140439
